Grid-Based Object Tracking

Abstract

Mobile robots require an accurate environment perception to plan intelligent maneuvers and avoid collisions. This thesis presents a novel multi sensor environment estimation strategy that fully combines tracking moving objects and mapping the static environment. The basic idea is to fuse and accumulate measurement data by a dynamic occupancy grid model, whereas moving objects are extracted subsequently based on that generic low-level grid representation. Overall, this work results in a robust and consistent estimation of arbitrary objects and obstacles, which is demonstrated in the context of autonomous driving in complex unstructured environments.

Contents

Notations VIII

Abstract XI

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Challenges of Multi-Sensor Environment Perception . . . . . . . . . . . . . 2

1.3 Main Contribution and Outline of This Work . . . . . . . . . . . . . . . . 8

2 Measurement Grid Representation and Fusion 13

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.2...